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AutomateLab n8n MCP

by tech.automatelab.mcp
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Generate an n8n workflow from a description

workflow_generate
Read-only

Generate a valid n8n workflow JSON from a plain-English description. Handles webhook/schedule/RSS triggers, common action nodes (Slack, Google Sheets, Discord, Gmail, Notion, HTTP), and AI Agent setups (LangChain root agent + chat model + memory + optional HTTP tool, wired with ai_languageModel / ai_memory / ai_tool connections). Returns workflow JSON with unique node IDs, connections, positions, and typeVersion on every node. Output is non-deterministic (random node IDs and webhook paths).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoOptional workflow name. Derived from the first sentence of the description if omitted.
descriptionYesPlain-English workflow description, e.g. 'Stripe webhook -> Slack message + Google Sheets row'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
workflowYesFull n8n workflow JSON (name, nodes, connections, settings, ...).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations mark readOnlyHint=true; description adds non-deterministic output and random IDs, providing context beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single paragraph, front-loaded main purpose, no wasted words, efficiently covers key points.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Describes return value (workflow JSON with fields) and behavior; output schema exists, but error cases not mentioned.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage 100%; description adds minor context (name derivation) but schema already documents parameters well.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Generate a valid n8n workflow JSON from a plain-English description' with specific triggers and nodes, distinguishing it from siblings like workflow_create.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Implied usage for generating workflows from text, but no explicit when-not or alternatives among siblings like workflow_create.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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